I write this blog with the aim to help researchers to write and publish scientific articles. The issues arise mostly from my experience in reading articles that I have got to edit. Secondly, they arise from researchers' efforts to publish articles.

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This blog post is the third part of the statistics theme. It’s easier if you read the previous posts first.

Sometimes, the statistical analyses are like a separate part of the study. This may be the case when someone else than the main author has done and written the statistical analysis. Usually, the Results section is better without a subtitle ‘Statistical analysis’.

It would be best to present both i) the actual values observed (measured) and ii) the statistical significance. In general, it is better to mix these two than to present them separately.

There are roughly two ways to write the results: 1) the focus is in the values observed, 2) the focus is in the significance and statistical analysis values. In other words: 1) starting from the values observed, 2) starting from the statistical analysis.

The difference is not great if both the actual values observed and statistical values, such as p values are presented. The problem will occur when either of them is not presented of when they are presented in separation from each other.

When the results have been written from purely a statistical analysis point of view, a reader has difficulties in assessing the actual results. Readers should always be able to see also the actual values observed, not only the statistical values. Readers should also see the variation around the means, so give the treatment means, SDs or SEs, and n.

How to mix the statistics and the values observed in practice? Try to start writing the values observed and then add between the statistics to show whether the results were significant or not. In other words, write first about the treatments and add the significance. Which of the treatments had the highest value and was it significantly different from the other treatments?

Think carefully what p values you should present. If you want to present many statistical analyses details, like F or MS values in ANOVA, make a table of them. In most cases, p values are enough.

However, the journal instructions about presenting the statistics vary. Some journals want details more, some less. Always read the instructions to authors and follow them. For any journal, however, ‘the more p and F values the better’ does not hold true – at least I hope so.